Introduction
Epilepsy is a disorder characterized by a recurrent risk of seizures, arising from a variety of heterogeneous causes [1]. A genetic etiology refers to pathogenic variants that directly cause epilepsy, typically presenting at an early age and resulting in pathological conditions [2,3]. Epilepsy with a confirmed genetic cause, such as pathogenic mutations in SCN1A, ZEB2, or CACNA1A, predominantly manifests at an early age and frequently leads to pathological conditions [3,4]. Diagnostic approaches—including brain magnetic resonance imaging (MRI), electroencephalography (EEG), and genetic testing combined with clinical features—aim to clarify etiology and guide clinical application. While advances in genetic diagnostics, such as whole-exome sequencing and epilepsy-specific next-generation sequencing panels, have lowered barriers to accessibility and cost [4,5], integrating information from these diverse modalities remains crucial for both diagnostic confirmation and understanding epilepsy mechanisms. Previous research has demonstrated that EEG features can help identify genetic contributions in early-onset epilepsy [6,7]. Volumetric MRI has been recognized as a valuable diagnostic tool in various neurological disorders, including Alzheimer’s disease, frontotemporal dementia, and parkinsonism. In epilepsy, volumetric MRI has primarily been applied to focal epilepsy, particularly temporal lobe epilepsy [8,9]. However, relatively few studies have evaluated its utility in nonlesional or idiopathic childhood epilepsy [10,11]. Key MRI volumetric indicators, considered essential for imaging genetics research, include brain volume, gray matter (GM) volume, cortical thickness, and surface area [12-14].
In this study, we hypothesized that epilepsy of genetic origin may exhibit distinctive volumetric features on brain MRI, even in the absence of overt structural lesions. To test this, We compared global and regional volumetric indicators among children with epilepsy of genetic (genetic group) and non-genetic etiology (non-genetic group) and healthy controls.
Materials and Methods
This retrospective study was approved by our Institutional Review Board of Inje University Haeundae Paik Hospital (approval no. 2018-11-018), and the requirement for informed consent was waived.
1. Study design and population
Between May 2010 and December 2018, patients aged 1 to 15 years with epilepsy who underwent brain MRI were included if they met the following criteria: (1) diagnosis of epilepsy at our institution with genetic confirmation; (2) age-matched counterparts without a genetic diagnosis, congenital anomalies, or developmental delays, and with no family history of epilepsy or genetic diseases; and (3) age- and sex-matched healthy controls diagnosed with primary headache or non-lesional trauma. Exclusion criteria were: (1) children with brain MRI signal changes indicative of conditions such as hypoxic-ischemic encephalopathy, demyelinating disorders, brain tumors, or neurocutaneous disease; (2) cases with blurred interfaces between the GM and white matter (WM) or cortical thickening suggestive of lissencephaly, pachygyria, or polymicrogyria; (3) infants younger than 12 months; and (4) children who underwent MRI more than 12 months after epilepsy diagnosis. All images were reviewed by a pediatric neuroradiologist to confirm eligibility. A total of 73 children were included: 25 in the genetic group , 25 in the non-genetic group, and 23 in the healthy control group.
The genetic group was further classified into mutation-related epilepsy syndromes and chromosomal abnormalities, while the non-genetic group included epilepsy of unknown origin, idiopathic focal epilepsy, and non-lesional cases.
Medical records were reviewed for sex, age at seizure onset and at MRI, the number of anti-seizure medications (ASMs), and duration of the seizure-free period. We compared total and regional brain volumes, mean surface area, and cortical thickness across groups to determine significant differences. Additionally, among children who underwent follow-up MRI scans, we examined possible age-related changes (Ngenetic=6, Nnon-genetic=5).
2. MRI acquisition
MRI scans were performed using a Philips Achieva 3.0T scanner (Philips Healthcare, Eindhoven, The Netherlands) (n=57) and a GE Signa HDxt 1.5T scanner (GE HealthCare, Chicago, IL, USA) (n=16). For the Philips 3.0T Achieva, three-dimensional whole-brain T1-weighted images were acquired using the Magnetization Prepared Rapid Gradient Echo sequence with the following parameters: echo time (TE)=5.7 ms, repetition time (TR)=10.1 ms, inversion time=1,300 ms, flip angle (FA)=8.0°, field of view (FOV)=240×240 mm, slice thickness=1 mm, and in-plane resolution=1.0×1.0 mm. For the GE 1.5T Signa, three-dimensional T1-weighted images were obtained with TE=1.7 ms, TR=6.1 ms, FA=10.0°, FOV=200×200 mm, slice thickness=1 mm, and in-plane resolution=1.0×1.0 mm. All raw T1-weighted images were visually inspected by an experienced pediatric neuroradiologist for artifacts that could compromise segmentation accuracy.
3. Image analyses
Quantification of brain volume, mean surface area, and cortical thickness was performed using FreeSurfer (version 7.1.1; http://surfer.nmr.mgh.harvard.edu/) on a 64-bit Linux CentOS 5 system. The processing pipeline included volume registration to the Talairach atlas, bias field correction, initial volumetric labeling, nonlinear alignment to Talairach space, final volumetric reconstruction, and inflation of cortical surfaces. Cortical thickness was defined as the shortest distance between the GM/WM boundary and the GM/cerebrospinal fluid boundary at each vertex. Surface-based analysis provided automated estimates of brain volume, cortical thickness, and surface area across 34 regions per hemisphere, enabling precise comparisons across groups and the identification of region-specific brain changes.
4. Statistical analysis
Distributions of age and sex were compared among patients with epilepsy of genetic and non-genetic etiologies and healthy controls. Analysis of variance (ANOVA) was used for age, and the chi-square test was used for sex. Additional clinical characteristics of the three groups were also compared. For continuous variables, ANOVA or the t-test was applied, whereas categorical variables were analyzed using the chi-square test or the Fisher exact test. Differences in total and regional brain volume, mean surface area, and mean cortical thickness among groups were assessed using analysis of covariance (ANCOVA) in SPSS version 25.0 (IBM Corp., Armonk, NY, USA), with age and sex included as covariates. Adjusted P values from ANCOVA, together with post hoc test results, were used to identify groups showing significant differences. To control for type I error, the Bonferroni correction was applied to adjust P values, providing more rigorous protection against false positives. Finally, Pearson correlation coefficients were calculated to evaluate linear relationships between age and each volumetric parameter.
Results
1. Comparison of clinical data
The genetic group consisted of eight individuals with generalized epilepsy (KCNQ2-related, PIGT-related, 3q13.31 deletion, Turner syndrome, GNAO1-related, Pallister-Killian syndrome, ITPR1-related, and 15q11-13 duplication), six individuals with Lennox-Gastaut syndrome (chromosome 1q44 microdeletion, two cases with CACNA1A mutation, ring chromosome 20, CHRNA2-related, and MECP2-related), and nine individuals with developmental and epileptic encephalopathy (Mowat-Wilson syndrome, KCNQ2-related, DISP1 deletion, HCN1-related, chromosome 3 monosomy, epilepsy in infancy with migrating focal seizures associated with an SETBP1 mutation, infantile epileptic spasms syndrome with a FOX mutation, and two cases of severe myoclonic epilepsy of infancy with PCDH19 mutation). Additionally, two individuals were diagnosed with generalized epilepsy with febrile seizures plus (SCN1B and SCN1A).
The non-genetic group included 12 patients with frontal lobe epilepsy, eight with temporal lobe epilepsy, three with parietal lobe epilepsy, and two with occipital lobe epilepsy.
A comparison of clinical data across the genetic, non-genetic, and control groups showed no statistically significant differences in sex (P=0.333) or age at MRI (P=0.440) (Table 1). Within the epilepsy subgroups, age at seizure onset (P=0.422) and seizure-free status (P=0.550) were not significantly different between genetic and non-genetic groups. However, the mean number of ASMs was significantly higher in the genetic group than in the non-genetic group (2.96±1.25 vs. 1.32±0.55, P<0.001).
2. Comparison of brain volume, mean surface area, and cortical thickness
Table 1 summarizes brain volume, mean surface area, and cortical thickness across the three groups. The genetic group had significantly smaller total brain, GM, cortical and subcortical GM, WM, supratentorial, cerebellar, and cerebellar GM volumes than both the non-genetic and control groups. The genetic group also showed significantly reduced mean surface area. In contrast, mean cortical thickness did not differ significantly among the three groups.
3. Comparison of mean surface area and GM volume across brain regions
In the analysis of mean surface area across 34 brain regions in each hemisphere, the genetic group showed significant reductions in 62 of 68 regions compared to the other two groups (P<0.05) (Table 2). The genetic group exhibited statistically significant reductions in mean surface area and GM volume across the lateral aspects of the frontal, temporal, parietal, and occipital lobes, relative to both the non-genetic and control groups. Regions without significant differences included the posterior cingulate and inferior temporal regions bilaterally, as well as the pars opercularis and superior temporal regions in the right hemisphere. A similar pattern was observed for GM volume: most regions showed significant reductions in the genetic group, except for the posterior cingulate and inferior temporal regions bilaterally (Table 3).
4. Comparison of cortical thickness across brain regions
When comparing the three groups, cortical thickness revealed different patterns. For instance, in the left superior frontal region, cortical thickness was 2.99±0.34, 3.06±0.18, and 3.20±0.20 in the genetic, non-genetic, and control groups, respectively, with a significant overall group difference (P=0.012, ANCOVA). However, pairwise comparisons yielded P values of 0.586 (genetic vs. non-genetic), 0.010 (genetic vs. control), and 0.106 (non-genetic vs. control). Regions in the left hemisphere where the genetic group showed significantly reduced cortical thickness compared with one or both of the other groups included the pars opercularis, pars orbitalis, lateral orbitofrontal, paracentral, rostral anterior cingulate, isthmus cingulate, superior temporal, inferior temporal, and temporal pole. In the right hemisphere, no regions demonstrated significant cortical thickness differences between the genetic group and the other groups (Table 4).
5. Longitudinal MRI analysis of brain volume changes
Among 11 patients with follow-up MRI examinations (six genetic and five non-genetic), age-related changes in brain volume were analyzed. Both groups exhibited age-dependent increases in total brain, subcortical GM, and WM volumes. The non-genetic group showed significant increases in total brain, subcortical GM, and WM volumes (P<0.001). In the genetic group, only subcortical GM volume increased significantly (R=0.77, P=0.0001). Interestingly, cortical GM volume showed divergent trends: the non-genetic group demonstrated a significant increase (R=0.77, P=0.009), while the genetic group showed a decrease (R=-0.42, P=0.13). Fig. 1 illustrates these differences. In summary, the non-genetic group exhibited consistent increases across all brain regions, whereas the genetic group demonstrated increases limited to subcortical GM and a decline in cortical GM volume.
Discussion
In this study, we found that children with epilepsy of a genetic etiology is associated with distinct brain MRI volumetric indicators compared to non-genetic group and healthy controls. Specifically, total brain, GM, cortical and subcortical GM, WM, supratentorial, cerebellar, and cerebellar GM volumes were considerably smaller in the genetic group than in the other two groups. In addition, the mean surface area was significantly reduced in the genetic group. By contrast, mean cortical thickness did not differ significantly among the three groups.
These findings parallel reports of reduced brain volume or brain atrophy in certain gene-specific epilepsy syndromes. For example, patients with SCN1A mutations have been shown to exhibit smaller total brain, WM, and GM volumes [15], and Mowat-Wilson syndrome demonstrates an abnormal corpus callosum, ventriculomegaly, and WM abnormalities [16].
In our comparative analysis of GM volume and mean surface area across 34 brain regions in each hemisphere, 62 of 68 regions showed significance in the genetic group (P<0.05). Compared to the non-genetic and control groups, the genetic group exhibited statistically significant reductions in mean surface area and gray matter volume across the lateral regions of the frontal, temporal, parietal, and occipital lobes. Comparing GM volume across functional brain regions provides valuable insights into structural and functional differences. Variations in GM volume have been linked to cognitive capacity, developmental progression, and vulnerability to neurological disorders. For instance, increased GM volume in specific regions has been associated with enhanced memory, attention, and executive function [17], whereas reduced GM volume in particular areas has been linked to cognitive impairment and developmental abnormalities which is similar pattern with our study [18]. Similarly, differences in the surface area of functional groups may reflect variations in cognitive abilities, developmental trajectories, or susceptibility to neurological disorders. Studies have shown that larger surface areas in specific regions are correlated with better performance in working memory, attention, and visuospatial tasks [19]. Conversely, reduced surface areas in specific cortical regions have been associated with cognitive impairments and developmental abnormalities, reflecting a pattern consistent with the findings of our study [20,21].
A notable finding in our study was that the posterior cingulate and inferior temporal areas maintained preserved volumes, showing no significant differences among the three groups. The posterior cingulate cortex, with its extensive connectivity and high metabolic activity, is a key region in supporting internally directed cognition. The left-hemispheric inferior temporal region has been associated with the inscription of logographic and other non-alphabetic linguistic systems, signifying its integral role in complex language processing. Preserved volumes in certain brain regions can be attributed to various factors. It can be suggested possible that these areas are less affected by the genetic causes of epilepsy. This may also be understood as they may have a higher resilience or capacity for recovery than other regions.
The present study also demonstrated reduced cortical thickness in the genetic group across multiple regions, including the pars opercularis, pars orbitalis, lateral orbitofrontal, paracentral, rostral anterior cingulate, isthmus cingulate, superior temporal, inferior temporal, and temporal pole. Reductions in these regions may influence functions such as language processing, decision-making, emotional regulation, and sensory integration. Analyzing cortical thickness differences across functional regions is essential for understanding the interplay and connectivity between neural systems. For example, one study reported that cortical thickness in language-related regions varies among groups, a finding that provides important insights into how differences in neural architecture affect language processing [22].
Age exerts a strong influence on volumetric changes across distinct brain regions [23,24]. In our study, no statistically significant differences in mean age at the time of MRI were observed among the three groups. Even after adjusting for age in the correlation analysis using ANCOVA, the group-specific volumetric differences we identified are considered meaningful. Previous studies of normal brain development have shown that total brain volume increases rapidly between 12 and 24 months and then reaches approximately 1,400 mL, after which growth slows through early adolescence [25-27]. Matsui et al. [28] reported that while WM volume continues to increase throughout development, GM volume shows less pronounced changes beyond 48 months. Specifically, GM volume generally increases until 12-16 years of age, whereas WM continues to increase until approximately 20 years. In our study, total brain, subcortical GM, and WM volumes were reduced in both genetic and non-genetic groups compared with normative data, although these volumes increased with age in both groups. Importantly, the rate of increase was slower in the genetic group compared with the non-genetic group. Furthermore, unlike the developmental trajectory of other brain compartments, only the total cortical GM volume in the genetic group demonstrated a decreasing trend with age.
This study has several limitations. First, the retrospective design and narrowly defined inclusion criteria may have introduced selection bias, particularly in the recruitment of both epilepsy patients and healthy controls, potentially leading to overrepresentation or underrepresentation of certain subgroups.
Second, although the sample size of 73 children was sufficient to detect statistically significant differences, it nonetheless limits the generalizability of the findings. Larger cohorts would provide greater statistical power and enhance the reliability of the conclusions.
Third, the predominantly cross-sectional nature of the study constrained our ability to evaluate longitudinal changes in brain volumetry. Although a subset of patients underwent follow-up MRI, a prospective longitudinal design would better capture the progression of structural abnormalities associated with the genetic group, and the impact of therapeutic interventions.
Lastly, the absence of domain-specific cognitive and developmental assessments prevented correlation of volumetric findings with functional outcomes. Future prospective research incorporating individualized, domain-focused evaluations will be essential to elucidate structure-function relationships.
In conclusion, quantitative brain MRI volumetry may may help identify pediatric epilepsy patients with a possible genetic origin, particularly when reductions are observed in brain volume, gray matter volume, mean surface area or when gray matter volume shows a decreasing trend over time.
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